58 lines
2.2 KiB
Python
58 lines
2.2 KiB
Python
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from pprint import pprint
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import sklearn.metrics
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import autosklearn.regression
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import datetime
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import importlib
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import os
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import sys
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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import seaborn as sns
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import yaml
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from sklearn import linear_model, svm, kernel_ridge, gaussian_process
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from sklearn.model_selection import LeaveOneGroupOut, cross_val_score, train_test_split
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from sklearn.metrics import mean_squared_error, r2_score
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from sklearn.impute import SimpleImputer
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model_input = pd.read_csv("data/processed/models/population_model/input_PANAS_negative_affect_mean.csv") # Standardizirani podatki
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model_input.dropna(axis=1, how="all", inplace=True)
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model_input.dropna(axis=0, how="any", subset=["target"], inplace=True)
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categorical_feature_colnames = ["gender", "startlanguage"]
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categorical_feature_colnames += [col for col in model_input.columns if "mostcommonactivity" in col or "homelabel" in col]
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categorical_features = model_input[categorical_feature_colnames].copy()
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mode_categorical_features = categorical_features.mode().iloc[0]
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categorical_features = categorical_features.fillna(mode_categorical_features)
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categorical_features = categorical_features.apply(lambda col: col.astype("category"))
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if not categorical_features.empty:
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categorical_features = pd.get_dummies(categorical_features)
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numerical_features = model_input.drop(categorical_feature_colnames, axis=1)
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model_in = pd.concat([numerical_features, categorical_features], axis=1)
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index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
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model_in.set_index(index_columns, inplace=True)
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X_train, X_test, y_train, y_test = train_test_split(model_in.drop(["target", "pid"], axis=1), model_in["target"], test_size=0.30)
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automl = autosklearn.regression.AutoSklearnRegressor(
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time_left_for_this_task=7200,
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per_run_time_limit=120
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)
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automl.fit(X_train, y_train, dataset_name='straw')
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print(automl.leaderboard())
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pprint(automl.show_models(), indent=4)
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train_predictions = automl.predict(X_train)
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print("Train R2 score:", sklearn.metrics.r2_score(y_train, train_predictions))
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test_predictions = automl.predict(X_test)
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print("Test R2 score:", sklearn.metrics.r2_score(y_test, test_predictions))
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import sys
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sys.exit()
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